Many applications could benefit from the availability of human-like agents. Having access to such agents may be especially valuable in cooperative self-play scenarios, where training with them is crucial for successful coordination with people [1]}, [2]}. The traditional path towards constructing agents that learn and think like people is to take inspiration from the cognitive processes of the human mind and incorporate them into existing systems [3]}. In this article, we have pursued a different approach. Instead of hard-coding cognitive processes directly into our agents, we have identified two computational principles – meta-learning and resource rationality – that give rise to many aspects of human behavior. The presented approach is very general, easy to adapt to new domains, and can be scaled to more complex problem settings without major modifications. Finally, we want to emphasize that low description lengths might not only be a biological necessity, but also a feature. Implementing an algorithm in just a few bits acts as a strong form of regularization and could, in turn, produce exploration strategies that are applicable across domains. Hence, we believe that constructing artificial systems with such constraints could lead us towards more generally capable agents.
